Overview

Brought to you by YData

Dataset statistics

Number of variables29
Number of observations6687
Missing cells9836
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory188.0 B

Variable types

Text3
Categorical13
Numeric13

Alerts

Account Length (in months) is highly overall correlated with Contract Type and 3 other fieldsHigh correlation
Age is highly overall correlated with Senior and 1 other fieldsHigh correlation
Avg Monthly GB Download is highly overall correlated with Under 30High correlation
Churn Category is highly overall correlated with Churn Label and 1 other fieldsHigh correlation
Churn Label is highly overall correlated with Churn Category and 2 other fieldsHigh correlation
Churn Reason is highly overall correlated with Churn Category and 1 other fieldsHigh correlation
Contract Type is highly overall correlated with Account Length (in months)High correlation
Customer Service Calls is highly overall correlated with Churn LabelHigh correlation
Device Protection & Online Backup is highly overall correlated with Total ChargesHigh correlation
Extra International Charges is highly overall correlated with Intl Active and 2 other fieldsHigh correlation
Group is highly overall correlated with Number of Customers in GroupHigh correlation
Intl Active is highly overall correlated with Extra International Charges and 2 other fieldsHigh correlation
Intl Calls is highly overall correlated with Extra International Charges and 2 other fieldsHigh correlation
Intl Mins is highly overall correlated with Extra International Charges and 2 other fieldsHigh correlation
Local Calls is highly overall correlated with Account Length (in months) and 2 other fieldsHigh correlation
Local Mins is highly overall correlated with Account Length (in months) and 2 other fieldsHigh correlation
Monthly Charge is highly overall correlated with Total Charges and 1 other fieldsHigh correlation
Number of Customers in Group is highly overall correlated with GroupHigh correlation
Senior is highly overall correlated with AgeHigh correlation
Total Charges is highly overall correlated with Account Length (in months) and 4 other fieldsHigh correlation
Under 30 is highly overall correlated with Age and 1 other fieldsHigh correlation
Unlimited Data Plan is highly overall correlated with Monthly ChargeHigh correlation
Intl Plan is highly imbalanced (53.9%)Imbalance
Churn Category has 4918 (73.5%) missing valuesMissing
Churn Reason has 4918 (73.5%) missing valuesMissing
Customer ID has unique valuesUnique
Intl Calls has 4116 (61.6%) zerosZeros
Intl Mins has 4116 (61.6%) zerosZeros
Extra International Charges has 4464 (66.8%) zerosZeros
Customer Service Calls has 4056 (60.7%) zerosZeros
Avg Monthly GB Download has 1485 (22.2%) zerosZeros
Extra Data Charges has 5999 (89.7%) zerosZeros
Number of Customers in Group has 5166 (77.3%) zerosZeros

Reproduction

Analysis started2024-11-27 16:31:05.059507
Analysis finished2024-11-27 16:31:38.890212
Duration33.83 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Customer ID
Text

UNIQUE 

Distinct6687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:39.191332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters60183
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6687 ?
Unique (%)100.0%

Sample

1st row4444-BZPU
2nd row5676-PTZX
3rd row8532-ZEKQ
4th row1314-SMPJ
5th row2956-TXCJ
ValueCountFrequency (%)
4444-bzpu 1
 
< 0.1%
7055-cgmk 1
 
< 0.1%
8532-zekq 1
 
< 0.1%
1314-smpj 1
 
< 0.1%
2956-txcj 1
 
< 0.1%
9152-depy 1
 
< 0.1%
1958-sdso 1
 
< 0.1%
8787-qzuc 1
 
< 0.1%
7768-oqje 1
 
< 0.1%
7716-rheb 1
 
< 0.1%
Other values (6677) 6677
99.9%
2024-11-27T22:01:39.850318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 6687
 
11.1%
5 2763
 
4.6%
7 2714
 
4.5%
3 2702
 
4.5%
0 2683
 
4.5%
1 2683
 
4.5%
2 2677
 
4.4%
4 2671
 
4.4%
8 2662
 
4.4%
9 2605
 
4.3%
Other values (27) 29336
48.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26748
44.4%
Uppercase Letter 26748
44.4%
Dash Punctuation 6687
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 1127
 
4.2%
Z 1105
 
4.1%
M 1078
 
4.0%
D 1074
 
4.0%
V 1063
 
4.0%
Y 1056
 
3.9%
B 1053
 
3.9%
E 1048
 
3.9%
U 1045
 
3.9%
Q 1045
 
3.9%
Other values (16) 16054
60.0%
Decimal Number
ValueCountFrequency (%)
5 2763
10.3%
7 2714
10.1%
3 2702
10.1%
0 2683
10.0%
1 2683
10.0%
2 2677
10.0%
4 2671
10.0%
8 2662
10.0%
9 2605
9.7%
6 2588
9.7%
Dash Punctuation
ValueCountFrequency (%)
- 6687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33435
55.6%
Latin 26748
44.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 1127
 
4.2%
Z 1105
 
4.1%
M 1078
 
4.0%
D 1074
 
4.0%
V 1063
 
4.0%
Y 1056
 
3.9%
B 1053
 
3.9%
E 1048
 
3.9%
U 1045
 
3.9%
Q 1045
 
3.9%
Other values (16) 16054
60.0%
Common
ValueCountFrequency (%)
- 6687
20.0%
5 2763
8.3%
7 2714
8.1%
3 2702
8.1%
0 2683
8.0%
1 2683
8.0%
2 2677
8.0%
4 2671
 
8.0%
8 2662
 
8.0%
9 2605
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 6687
 
11.1%
5 2763
 
4.6%
7 2714
 
4.5%
3 2702
 
4.5%
0 2683
 
4.5%
1 2683
 
4.5%
2 2677
 
4.4%
4 2671
 
4.4%
8 2662
 
4.4%
9 2605
 
4.3%
Other values (27) 29336
48.7%

Churn Label
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
4891 
1
1796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4891
73.1%
1 1796
 
26.9%

Length

2024-11-27T22:01:40.067409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:40.239243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4891
73.1%
1 1796
 
26.9%

Most occurring characters

ValueCountFrequency (%)
0 4891
73.1%
1 1796
 
26.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4891
73.1%
1 1796
 
26.9%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4891
73.1%
1 1796
 
26.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4891
73.1%
1 1796
 
26.9%

Account Length (in months)
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.33782
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:40.395456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median29
Q355
95-th percentile71
Maximum77
Range76
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.595689
Coefficient of variation (CV)0.76058589
Kurtosis-1.3788335
Mean32.33782
Median Absolute Deviation (MAD)22
Skewness0.24987562
Sum216243
Variance604.94793
MonotonicityNot monotonic
2024-11-27T22:01:40.567345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 591
 
8.8%
2 225
 
3.4%
3 188
 
2.8%
71 167
 
2.5%
4 161
 
2.4%
70 143
 
2.1%
5 138
 
2.1%
72 137
 
2.0%
7 125
 
1.9%
9 122
 
1.8%
Other values (67) 4690
70.1%
ValueCountFrequency (%)
1 591
8.8%
2 225
 
3.4%
3 188
 
2.8%
4 161
 
2.4%
5 138
 
2.1%
6 97
 
1.5%
7 125
 
1.9%
8 115
 
1.7%
9 122
 
1.8%
10 103
 
1.5%
ValueCountFrequency (%)
77 5
 
0.1%
76 3
 
< 0.1%
75 14
 
0.2%
74 39
 
0.6%
73 102
1.5%
72 137
2.0%
71 167
2.5%
70 143
2.1%
69 99
1.5%
68 95
1.4%

Local Calls
Real number (ℝ)

HIGH CORRELATION 

Distinct521
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.97413
Minimum1
Maximum918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:40.770668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median98
Q3199
95-th percentile373
Maximum918
Range917
Interquartile range (IQR)168

Descriptive statistics

Standard deviation121.89397
Coefficient of variation (CV)0.93067209
Kurtosis1.1661736
Mean130.97413
Median Absolute Deviation (MAD)77
Skewness1.1669733
Sum875824
Variance14858.139
MonotonicityNot monotonic
2024-11-27T22:01:40.958546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 143
 
2.1%
4 134
 
2.0%
5 126
 
1.9%
2 115
 
1.7%
3 109
 
1.6%
7 91
 
1.4%
8 68
 
1.0%
9 66
 
1.0%
10 63
 
0.9%
12 48
 
0.7%
Other values (511) 5724
85.6%
ValueCountFrequency (%)
1 20
 
0.3%
2 115
1.7%
3 109
1.6%
4 134
2.0%
5 126
1.9%
6 143
2.1%
7 91
1.4%
8 68
1.0%
9 66
1.0%
10 63
0.9%
ValueCountFrequency (%)
918 1
< 0.1%
798 1
< 0.1%
722 1
< 0.1%
697 1
< 0.1%
694 1
< 0.1%
677 1
< 0.1%
676 1
< 0.1%
641 1
< 0.1%
637 1
< 0.1%
615 1
< 0.1%

Local Mins
Real number (ℝ)

HIGH CORRELATION 

Distinct4222
Distinct (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.75286
Minimum4
Maximum1234.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:41.115040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q176.9
median250.5
Q3498.05
95-th percentile911.74
Maximum1234.2
Range1230.2
Interquartile range (IQR)421.15

Descriptive statistics

Standard deviation288.61993
Coefficient of variation (CV)0.89424437
Kurtosis-0.030940292
Mean322.75286
Median Absolute Deviation (MAD)194.2
Skewness0.91633407
Sum2158248.4
Variance83301.465
MonotonicityNot monotonic
2024-11-27T22:01:41.302489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 57
 
0.9%
14 57
 
0.9%
7 54
 
0.8%
11 49
 
0.7%
4 48
 
0.7%
13 47
 
0.7%
10 46
 
0.7%
12 46
 
0.7%
16 45
 
0.7%
5 40
 
0.6%
Other values (4212) 6198
92.7%
ValueCountFrequency (%)
4 48
0.7%
5 40
0.6%
6 34
0.5%
6.4 2
 
< 0.1%
6.9 2
 
< 0.1%
7 54
0.8%
7.4 1
 
< 0.1%
7.6 1
 
< 0.1%
7.9 2
 
< 0.1%
8 36
0.5%
ValueCountFrequency (%)
1234.2 1
< 0.1%
1188.6 1
< 0.1%
1179.8 1
< 0.1%
1176.6 1
< 0.1%
1171.3 1
< 0.1%
1166.1 1
< 0.1%
1165.6 1
< 0.1%
1162.9 2
< 0.1%
1158.8 1
< 0.1%
1158.1 2
< 0.1%

Intl Calls
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct335
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.097524
Minimum0
Maximum1120
Zeros4116
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:41.490336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q352
95-th percentile276
Maximum1120
Range1120
Interquartile range (IQR)52

Descriptive statistics

Standard deviation103.59237
Coefficient of variation (CV)2.0273462
Kurtosis12.746142
Mean51.097524
Median Absolute Deviation (MAD)0
Skewness2.9593213
Sum341689.14
Variance10731.379
MonotonicityNot monotonic
2024-11-27T22:01:41.693835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4116
61.6%
4 168
 
2.5%
8 75
 
1.1%
12 68
 
1.0%
16 60
 
0.9%
36 42
 
0.6%
20 40
 
0.6%
28 39
 
0.6%
284 35
 
0.5%
32 35
 
0.5%
Other values (325) 2009
30.0%
ValueCountFrequency (%)
0 4116
61.6%
1 4
 
0.1%
2 11
 
0.2%
3 22
 
0.3%
4 168
 
2.5%
4.08 1
 
< 0.1%
4.2 1
 
< 0.1%
5 12
 
0.2%
5.880735125 2
 
< 0.1%
6 19
 
0.3%
ValueCountFrequency (%)
1120 1
< 0.1%
1050 1
< 0.1%
975 1
< 0.1%
962 1
< 0.1%
910 1
< 0.1%
896 1
< 0.1%
828 1
< 0.1%
825 1
< 0.1%
816 1
< 0.1%
768 1
< 0.1%

Intl Mins
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1626
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.07062
Minimum0
Maximum1372.5
Zeros4116
Zeros (%)61.6%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:41.928500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3140.4
95-th percentile720.35
Maximum1372.5
Range1372.5
Interquartile range (IQR)140.4

Descriptive statistics

Standard deviation243.52783
Coefficient of variation (CV)1.8722738
Kurtosis2.9525347
Mean130.07062
Median Absolute Deviation (MAD)0
Skewness1.9703145
Sum869782.26
Variance59305.803
MonotonicityNot monotonic
2024-11-27T22:01:42.100334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4116
61.6%
9.7 12
 
0.2%
12.8 11
 
0.2%
12.1 9
 
0.1%
13.3 7
 
0.1%
14 6
 
0.1%
53.6 6
 
0.1%
594 6
 
0.1%
10.7 6
 
0.1%
335.4 6
 
0.1%
Other values (1616) 2502
37.4%
ValueCountFrequency (%)
0 4116
61.6%
1 3
 
< 0.1%
2.4 1
 
< 0.1%
3 1
 
< 0.1%
5.1 1
 
< 0.1%
5.4 1
 
< 0.1%
5.5 1
 
< 0.1%
5.7 1
 
< 0.1%
5.8 1
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
1372.5 1
< 0.1%
1225 1
< 0.1%
1224 1
< 0.1%
1214.1 1
< 0.1%
1171.5 1
< 0.1%
1164.4 1
< 0.1%
1161.8 1
< 0.1%
1144.6 1
< 0.1%
1134.6 1
< 0.1%
1128.6 2
< 0.1%

Intl Active
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
4116 
1
2571 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 4116
61.6%
1 2571
38.4%

Length

2024-11-27T22:01:42.272708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:42.430065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4116
61.6%
1 2571
38.4%

Most occurring characters

ValueCountFrequency (%)
0 4116
61.6%
1 2571
38.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4116
61.6%
1 2571
38.4%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4116
61.6%
1 2571
38.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4116
61.6%
1 2571
38.4%

Intl Plan
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
6036 
1
651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 6036
90.3%
1 651
 
9.7%

Length

2024-11-27T22:01:42.539812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:42.680635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 6036
90.3%
1 651
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 6036
90.3%
1 651
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6036
90.3%
1 651
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6036
90.3%
1 651
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6036
90.3%
1 651
 
9.7%

Extra International Charges
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1290
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.641783
Minimum0
Maximum585.8
Zeros4464
Zeros (%)66.8%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:42.836864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316.4
95-th percentile204
Maximum585.8
Range585.8
Interquartile range (IQR)16.4

Descriptive statistics

Standard deviation76.346828
Coefficient of variation (CV)2.269405
Kurtosis9.5904293
Mean33.641783
Median Absolute Deviation (MAD)0
Skewness2.9422055
Sum224962.6
Variance5828.8382
MonotonicityNot monotonic
2024-11-27T22:01:42.977418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4464
66.8%
2.6 13
 
0.2%
4.3 13
 
0.2%
2.5 10
 
0.1%
3.2 10
 
0.1%
2.8 10
 
0.1%
3.7 9
 
0.1%
4.7 9
 
0.1%
5.3 9
 
0.1%
2.9 8
 
0.1%
Other values (1280) 2132
31.9%
ValueCountFrequency (%)
0 4464
66.8%
0.2 2
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.8 1
 
< 0.1%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.4 3
 
< 0.1%
1.5 1
 
< 0.1%
1.6 3
 
< 0.1%
ValueCountFrequency (%)
585.8 1
< 0.1%
582.2 1
< 0.1%
539.6 1
< 0.1%
529.2 1
< 0.1%
525.6 1
< 0.1%
520.8 1
< 0.1%
509.2 1
< 0.1%
506.3 1
< 0.1%
486.4 1
< 0.1%
483 1
< 0.1%

Customer Service Calls
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91565725
Minimum0
Maximum5
Zeros4056
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:43.119477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.411484
Coefficient of variation (CV)1.5414982
Kurtosis1.3959339
Mean0.91565725
Median Absolute Deviation (MAD)0
Skewness1.5409945
Sum6123
Variance1.992287
MonotonicityNot monotonic
2024-11-27T22:01:43.221618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 4056
60.7%
1 889
 
13.3%
2 863
 
12.9%
3 297
 
4.4%
4 293
 
4.4%
5 289
 
4.3%
ValueCountFrequency (%)
0 4056
60.7%
1 889
 
13.3%
2 863
 
12.9%
3 297
 
4.4%
4 293
 
4.4%
5 289
 
4.3%
ValueCountFrequency (%)
5 289
 
4.3%
4 293
 
4.4%
3 297
 
4.4%
2 863
 
12.9%
1 889
 
13.3%
0 4056
60.7%

Avg Monthly GB Download
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6962764
Minimum0
Maximum43
Zeros1485
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:43.355044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q39
95-th percentile24
Maximum43
Range43
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.4543394
Coefficient of variation (CV)1.1132067
Kurtosis3.7214425
Mean6.6962764
Median Absolute Deviation (MAD)4
Skewness1.7934869
Sum44778
Variance55.567176
MonotonicityNot monotonic
2024-11-27T22:01:43.495634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 1485
22.2%
5 523
 
7.8%
4 474
 
7.1%
3 469
 
7.0%
2 433
 
6.5%
6 429
 
6.4%
1 377
 
5.6%
7 326
 
4.9%
8 260
 
3.9%
9 250
 
3.7%
Other values (26) 1661
24.8%
ValueCountFrequency (%)
0 1485
22.2%
1 377
 
5.6%
2 433
 
6.5%
3 469
 
7.0%
4 474
 
7.1%
5 523
 
7.8%
6 429
 
6.4%
7 326
 
4.9%
8 260
 
3.9%
9 250
 
3.7%
ValueCountFrequency (%)
43 8
 
0.1%
41 8
 
0.1%
38 21
0.3%
37 19
 
0.3%
36 9
 
0.1%
35 20
0.3%
30 49
0.7%
29 24
0.4%
28 17
 
0.3%
27 35
0.5%

Unlimited Data Plan
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
1
4494 
0
2193 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4494
67.2%
0 2193
32.8%

Length

2024-11-27T22:01:43.620634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:43.751940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4494
67.2%
0 2193
32.8%

Most occurring characters

ValueCountFrequency (%)
1 4494
67.2%
0 2193
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4494
67.2%
0 2193
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4494
67.2%
0 2193
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4494
67.2%
0 2193
32.8%

Extra Data Charges
Real number (ℝ)

ZEROS 

Distinct91
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3744579
Minimum0
Maximum99
Zeros5999
Zeros (%)89.7%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:43.891245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile29.7
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.565309
Coefficient of variation (CV)3.7236525
Kurtosis19.53365
Mean3.3744579
Median Absolute Deviation (MAD)0
Skewness4.3364777
Sum22565
Variance157.88698
MonotonicityNot monotonic
2024-11-27T22:01:44.063368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5999
89.7%
6 42
 
0.6%
5 41
 
0.6%
4 33
 
0.5%
7 19
 
0.3%
19 17
 
0.3%
13 16
 
0.2%
38 15
 
0.2%
12 14
 
0.2%
23 13
 
0.2%
Other values (81) 478
 
7.1%
ValueCountFrequency (%)
0 5999
89.7%
3 6
 
0.1%
4 33
 
0.5%
5 41
 
0.6%
6 42
 
0.6%
7 19
 
0.3%
8 7
 
0.1%
9 7
 
0.1%
10 6
 
0.1%
11 9
 
0.1%
ValueCountFrequency (%)
99 3
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
93 1
 
< 0.1%
92 2
< 0.1%
91 1
 
< 0.1%
90 2
< 0.1%
88 3
< 0.1%
87 1
 
< 0.1%
83 3
< 0.1%

State
Text

Distinct51
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:44.281758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters13374
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowOH
4th rowMO
5th rowWV
ValueCountFrequency (%)
wv 213
 
3.2%
mn 168
 
2.5%
ny 167
 
2.5%
al 161
 
2.4%
oh 158
 
2.4%
wi 156
 
2.3%
or 156
 
2.3%
va 155
 
2.3%
wy 154
 
2.3%
ct 148
 
2.2%
Other values (41) 5051
75.5%
2024-11-27T22:01:44.710970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1471
 
11.0%
A 1380
 
10.3%
M 1227
 
9.2%
I 1035
 
7.7%
T 827
 
6.2%
D 761
 
5.7%
C 712
 
5.3%
O 696
 
5.2%
W 655
 
4.9%
V 647
 
4.8%
Other values (14) 3963
29.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13374
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1471
 
11.0%
A 1380
 
10.3%
M 1227
 
9.2%
I 1035
 
7.7%
T 827
 
6.2%
D 761
 
5.7%
C 712
 
5.3%
O 696
 
5.2%
W 655
 
4.9%
V 647
 
4.8%
Other values (14) 3963
29.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 13374
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1471
 
11.0%
A 1380
 
10.3%
M 1227
 
9.2%
I 1035
 
7.7%
T 827
 
6.2%
D 761
 
5.7%
C 712
 
5.3%
O 696
 
5.2%
W 655
 
4.9%
V 647
 
4.8%
Other values (14) 3963
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1471
 
11.0%
A 1380
 
10.3%
M 1227
 
9.2%
I 1035
 
7.7%
T 827
 
6.2%
D 761
 
5.7%
C 712
 
5.3%
O 696
 
5.2%
W 655
 
4.9%
V 647
 
4.8%
Other values (14) 3963
29.6%
Distinct6677
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:45.099602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.0276656
Min length7

Characters and Unicode

Total characters53681
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6667 ?
Unique (%)99.7%

Sample

1st row382-4657
2nd row371-7191
3rd row375-9999
4th row329-9001
5th row330-8173
ValueCountFrequency (%)
359-9794 2
 
< 0.1%
277-4048 2
 
< 0.1%
334-9818 2
 
< 0.1%
329-6144 2
 
< 0.1%
390-3401 2
 
< 0.1%
311-9063 2
 
< 0.1%
333-7803 2
 
< 0.1%
312-3187 2
 
< 0.1%
365-9011 2
 
< 0.1%
313-7716 2
 
< 0.1%
Other values (6667) 6667
99.7%
2024-11-27T22:01:45.631390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 9057
16.9%
- 6687
12.5%
4 4943
9.2%
2 4830
9.0%
9 4176
7.8%
5 4175
7.8%
1 4089
7.6%
8 4057
7.6%
6 4047
7.5%
7 4033
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46994
87.5%
Dash Punctuation 6687
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 9057
19.3%
4 4943
10.5%
2 4830
10.3%
9 4176
8.9%
5 4175
8.9%
1 4089
8.7%
8 4057
8.6%
6 4047
8.6%
7 4033
8.6%
0 3587
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 6687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53681
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 9057
16.9%
- 6687
12.5%
4 4943
9.2%
2 4830
9.0%
9 4176
7.8%
5 4175
7.8%
1 4089
7.6%
8 4057
7.6%
6 4047
7.5%
7 4033
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 9057
16.9%
- 6687
12.5%
4 4943
9.2%
2 4830
9.0%
9 4176
7.8%
5 4175
7.8%
1 4089
7.6%
8 4057
7.6%
6 4047
7.5%
7 4033
7.5%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
1
3379 
0
3301 
2
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 3379
50.5%
0 3301
49.4%
2 7
 
0.1%

Length

2024-11-27T22:01:45.829652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:45.997465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3379
50.5%
0 3301
49.4%
2 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 3379
50.5%
0 3301
49.4%
2 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3379
50.5%
0 3301
49.4%
2 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3379
50.5%
0 3301
49.4%
2 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3379
50.5%
0 3301
49.4%
2 7
 
0.1%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.448632
Minimum19
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:46.157388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q133
median47
Q360
95-th percentile77
Maximum85
Range66
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.969893
Coefficient of variation (CV)0.35764769
Kurtosis-0.92804108
Mean47.448632
Median Absolute Deviation (MAD)14
Skewness0.21438577
Sum317289
Variance287.97728
MonotonicityNot monotonic
2024-11-27T22:01:46.338995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 194
 
2.9%
48 179
 
2.7%
21 147
 
2.2%
47 139
 
2.1%
45 139
 
2.1%
25 139
 
2.1%
41 138
 
2.1%
38 138
 
2.1%
43 136
 
2.0%
46 131
 
2.0%
Other values (57) 5207
77.9%
ValueCountFrequency (%)
19 50
 
0.7%
20 63
0.9%
21 147
2.2%
22 118
1.8%
23 125
1.9%
24 110
1.6%
25 139
2.1%
26 111
1.7%
27 121
1.8%
28 109
1.6%
ValueCountFrequency (%)
85 25
 
0.4%
84 40
0.6%
83 28
0.4%
82 40
0.6%
81 36
0.5%
80 35
0.5%
79 38
0.6%
78 47
0.7%
77 66
1.0%
76 60
0.9%

Under 30
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
5400 
1
1287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5400
80.8%
1 1287
 
19.2%

Length

2024-11-27T22:01:47.004062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:47.184907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5400
80.8%
1 1287
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 5400
80.8%
1 1287
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5400
80.8%
1 1287
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5400
80.8%
1 1287
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5400
80.8%
1 1287
 
19.2%

Senior
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
5460 
1
1227 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5460
81.7%
1 1227
 
18.3%

Length

2024-11-27T22:01:47.342770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:47.527961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5460
81.7%
1 1227
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 5460
81.7%
1 1227
 
18.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5460
81.7%
1 1227
 
18.3%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5460
81.7%
1 1227
 
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5460
81.7%
1 1227
 
18.3%

Group
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
5166 
1
1521 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5166
77.3%
1 1521
 
22.7%

Length

2024-11-27T22:01:47.706150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:47.886757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5166
77.3%
1 1521
 
22.7%

Most occurring characters

ValueCountFrequency (%)
0 5166
77.3%
1 1521
 
22.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5166
77.3%
1 1521
 
22.7%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5166
77.3%
1 1521
 
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5166
77.3%
1 1521
 
22.7%

Number of Customers in Group
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83789442
Minimum0
Maximum6
Zeros5166
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:48.015026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7007801
Coefficient of variation (CV)2.0298263
Kurtosis2.297229
Mean0.83789442
Median Absolute Deviation (MAD)0
Skewness1.8989939
Sum5603
Variance2.8926529
MonotonicityNot monotonic
2024-11-27T22:01:48.136654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5166
77.3%
2 492
 
7.4%
3 270
 
4.0%
6 268
 
4.0%
4 254
 
3.8%
5 237
 
3.5%
ValueCountFrequency (%)
0 5166
77.3%
2 492
 
7.4%
3 270
 
4.0%
4 254
 
3.8%
5 237
 
3.5%
6 268
 
4.0%
ValueCountFrequency (%)
6 268
 
4.0%
5 237
 
3.5%
4 254
 
3.8%
3 270
 
4.0%
2 492
 
7.4%
0 5166
77.3%

Device Protection & Online Backup
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
4393 
1
2294 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4393
65.7%
1 2294
34.3%

Length

2024-11-27T22:01:48.371583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:48.521170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4393
65.7%
1 2294
34.3%

Most occurring characters

ValueCountFrequency (%)
0 4393
65.7%
1 2294
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4393
65.7%
1 2294
34.3%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4393
65.7%
1 2294
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4393
65.7%
1 2294
34.3%

Contract Type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
0
3411 
2
1797 
1
1479 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3411
51.0%
2 1797
26.9%
1 1479
22.1%

Length

2024-11-27T22:01:48.660708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:48.830834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3411
51.0%
2 1797
26.9%
1 1479
22.1%

Most occurring characters

ValueCountFrequency (%)
0 3411
51.0%
2 1797
26.9%
1 1479
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3411
51.0%
2 1797
26.9%
1 1479
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3411
51.0%
2 1797
26.9%
1 1479
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3411
51.0%
2 1797
26.9%
1 1479
22.1%

Payment Method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size52.4 KiB
1
3702 
0
2614 
2
371 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6687
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 3702
55.4%
0 2614
39.1%
2 371
 
5.5%

Length

2024-11-27T22:01:48.983614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:49.153708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3702
55.4%
0 2614
39.1%
2 371
 
5.5%

Most occurring characters

ValueCountFrequency (%)
1 3702
55.4%
0 2614
39.1%
2 371
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3702
55.4%
0 2614
39.1%
2 371
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 6687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3702
55.4%
0 2614
39.1%
2 371
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3702
55.4%
0 2614
39.1%
2 371
 
5.5%

Monthly Charge
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.030357
Minimum5
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:49.305399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q116
median31
Q343
95-th percentile59
Maximum78
Range73
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.288147
Coefficient of variation (CV)0.52491008
Kurtosis-0.8720226
Mean31.030357
Median Absolute Deviation (MAD)13
Skewness0.20551241
Sum207500
Variance265.30374
MonotonicityNot monotonic
2024-11-27T22:01:49.499002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 249
 
3.7%
9 233
 
3.5%
8 226
 
3.4%
7 192
 
2.9%
12 174
 
2.6%
11 168
 
2.5%
13 165
 
2.5%
35 163
 
2.4%
34 155
 
2.3%
39 155
 
2.3%
Other values (64) 4807
71.9%
ValueCountFrequency (%)
5 12
 
0.2%
6 74
 
1.1%
7 192
2.9%
8 226
3.4%
9 233
3.5%
10 249
3.7%
11 168
2.5%
12 174
2.6%
13 165
2.5%
14 61
 
0.9%
ValueCountFrequency (%)
78 1
 
< 0.1%
77 1
 
< 0.1%
76 2
 
< 0.1%
75 3
 
< 0.1%
74 2
 
< 0.1%
73 5
0.1%
72 5
0.1%
71 3
 
< 0.1%
70 12
0.2%
69 12
0.2%

Total Charges
Real number (ℝ)

HIGH CORRELATION 

Distinct2654
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1083.7556
Minimum6
Maximum5574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size52.4 KiB
2024-11-27T22:01:49.722323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile23.3
Q1181
median647
Q31732.5
95-th percentile3393.1
Maximum5574
Range5568
Interquartile range (IQR)1551.5

Descriptive statistics

Standard deviation1127.0749
Coefficient of variation (CV)1.0399714
Kurtosis0.68311597
Mean1083.7556
Median Absolute Deviation (MAD)565
Skewness1.1969999
Sum7247074
Variance1270297.7
MonotonicityNot monotonic
2024-11-27T22:01:49.940139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 34
 
0.5%
13 32
 
0.5%
26 32
 
0.5%
12 29
 
0.4%
27 27
 
0.4%
16 24
 
0.4%
37 23
 
0.3%
7 23
 
0.3%
9 22
 
0.3%
25 22
 
0.3%
Other values (2644) 6419
96.0%
ValueCountFrequency (%)
6 9
 
0.1%
7 23
0.3%
8 21
0.3%
9 22
0.3%
10 34
0.5%
11 19
0.3%
12 29
0.4%
13 32
0.5%
14 11
 
0.2%
15 9
 
0.1%
ValueCountFrequency (%)
5574 1
< 0.1%
5473 1
< 0.1%
5392 1
< 0.1%
5375 1
< 0.1%
5347 1
< 0.1%
5274 1
< 0.1%
5202 1
< 0.1%
5187 1
< 0.1%
5182 1
< 0.1%
5180 1
< 0.1%

Churn Category
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.3%
Missing4918
Missing (%)73.5%
Memory size52.4 KiB
Competitor
805 
Attitude
287 
Dissatisfaction
286 
Price
200 
Other
191 

Length

Max length15
Median length10
Mean length9.3787451
Min length5

Characters and Unicode

Total characters16591
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor
2nd rowCompetitor
3rd rowOther
4th rowCompetitor
5th rowCompetitor

Common Values

ValueCountFrequency (%)
Competitor 805
 
12.0%
Attitude 287
 
4.3%
Dissatisfaction 286
 
4.3%
Price 200
 
3.0%
Other 191
 
2.9%
(Missing) 4918
73.5%

Length

2024-11-27T22:01:50.152123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T22:01:50.332336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
competitor 805
45.5%
attitude 287
 
16.2%
dissatisfaction 286
 
16.2%
price 200
 
11.3%
other 191
 
10.8%

Most occurring characters

ValueCountFrequency (%)
t 3234
19.5%
i 2150
13.0%
o 1896
11.4%
e 1483
8.9%
r 1196
 
7.2%
s 858
 
5.2%
C 805
 
4.9%
m 805
 
4.9%
p 805
 
4.9%
a 572
 
3.4%
Other values (10) 2787
16.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14822
89.3%
Uppercase Letter 1769
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3234
21.8%
i 2150
14.5%
o 1896
12.8%
e 1483
10.0%
r 1196
 
8.1%
s 858
 
5.8%
m 805
 
5.4%
p 805
 
5.4%
a 572
 
3.9%
c 486
 
3.3%
Other values (5) 1337
9.0%
Uppercase Letter
ValueCountFrequency (%)
C 805
45.5%
A 287
 
16.2%
D 286
 
16.2%
P 200
 
11.3%
O 191
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 16591
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3234
19.5%
i 2150
13.0%
o 1896
11.4%
e 1483
8.9%
r 1196
 
7.2%
s 858
 
5.2%
C 805
 
4.9%
m 805
 
4.9%
p 805
 
4.9%
a 572
 
3.4%
Other values (10) 2787
16.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3234
19.5%
i 2150
13.0%
o 1896
11.4%
e 1483
8.9%
r 1196
 
7.2%
s 858
 
5.2%
C 805
 
4.9%
m 805
 
4.9%
p 805
 
4.9%
a 572
 
3.4%
Other values (10) 2787
16.8%

Churn Reason
Categorical

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)1.1%
Missing4918
Missing (%)73.5%
Memory size52.4 KiB
Competitor made better offer
303 
Competitor had better devices
297 
Attitude of support person
203 
Don't know
123 
Competitor offered more data
110 
Other values (15)
733 

Length

Max length41
Median length32
Mean length25.243075
Min length5

Characters and Unicode

Total characters44655
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor made better offer
2nd rowCompetitor made better offer
3rd rowMoved
4th rowCompetitor made better offer
5th rowCompetitor had better devices

Common Values

ValueCountFrequency (%)
Competitor made better offer 303
 
4.5%
Competitor had better devices 297
 
4.4%
Attitude of support person 203
 
3.0%
Don't know 123
 
1.8%
Competitor offered more data 110
 
1.6%
Competitor offered higher download speeds 95
 
1.4%
Attitude of service provider 84
 
1.3%
Price too high 74
 
1.1%
Product dissatisfaction 73
 
1.1%
Network reliability 69
 
1.0%
Other values (10) 338
 
5.1%
(Missing) 4918
73.5%

Length

2024-11-27T22:01:50.536782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor 805
 
12.8%
better 600
 
9.5%
of 417
 
6.6%
made 303
 
4.8%
offer 303
 
4.8%
had 297
 
4.7%
devices 297
 
4.7%
attitude 287
 
4.6%
support 244
 
3.9%
offered 205
 
3.3%
Other values (37) 2536
40.3%

Most occurring characters

ValueCountFrequency (%)
e 5823
13.0%
t 4936
11.1%
4525
10.1%
o 4401
 
9.9%
r 3503
 
7.8%
i 2755
 
6.2%
d 2397
 
5.4%
s 1799
 
4.0%
p 1783
 
4.0%
a 1727
 
3.9%
Other values (27) 11006
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38158
85.5%
Space Separator 4525
 
10.1%
Uppercase Letter 1795
 
4.0%
Other Punctuation 151
 
0.3%
Dash Punctuation 26
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5823
15.3%
t 4936
12.9%
o 4401
11.5%
r 3503
9.2%
i 2755
 
7.2%
d 2397
 
6.3%
s 1799
 
4.7%
p 1783
 
4.7%
a 1727
 
4.5%
f 1648
 
4.3%
Other values (13) 7386
19.4%
Uppercase Letter
ValueCountFrequency (%)
C 805
44.8%
A 287
 
16.0%
P 188
 
10.5%
L 150
 
8.4%
D 129
 
7.2%
N 69
 
3.8%
S 60
 
3.3%
M 44
 
2.5%
E 37
 
2.1%
W 26
 
1.4%
Other Punctuation
ValueCountFrequency (%)
' 123
81.5%
/ 28
 
18.5%
Space Separator
ValueCountFrequency (%)
4525
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39953
89.5%
Common 4702
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5823
14.6%
t 4936
12.4%
o 4401
11.0%
r 3503
 
8.8%
i 2755
 
6.9%
d 2397
 
6.0%
s 1799
 
4.5%
p 1783
 
4.5%
a 1727
 
4.3%
f 1648
 
4.1%
Other values (23) 9181
23.0%
Common
ValueCountFrequency (%)
4525
96.2%
' 123
 
2.6%
/ 28
 
0.6%
- 26
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5823
13.0%
t 4936
11.1%
4525
10.1%
o 4401
 
9.9%
r 3503
 
7.8%
i 2755
 
6.2%
d 2397
 
5.4%
s 1799
 
4.0%
p 1783
 
4.0%
a 1727
 
3.9%
Other values (27) 11006
24.6%

Interactions

2024-11-27T22:01:35.441150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:08.804484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.075584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.922352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:14.985898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:17.257180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:19.437635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:21.537593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.620700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:25.690981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:27.633877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:31.257015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.298577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:35.590393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:09.090365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.212543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.067200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:15.154635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:17.463326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:19.608317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:21.730487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.776585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:25.870560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:27.903908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:31.431450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.466290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:35.724253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:09.291999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.344254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.187984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:15.272995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:17.606844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:19.756834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:21.892553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.924245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.017219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:28.177679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:31.574501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.639184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:35.902287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:09.510030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.487074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.338459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:15.426473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:17.790116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:19.909538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:22.042255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:24.074106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.180086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:28.433954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:31.731331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.801124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:36.056757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:09.700701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.597668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.488028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:15.557920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:17.938682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:20.042387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:22.191090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:24.207222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.339906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:28.656834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:31.881741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.957593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:36.207145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:09.879342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.745618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.637943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:15.708808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:18.096801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:20.225574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:22.374873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:24.358064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.482005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:28.925392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:32.049591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:34.120026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:36.389280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.041928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:11.942667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.792013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:15.867348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:18.256936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:20.372097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:22.540150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:24.537868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.639286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:29.212612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:32.208631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:34.280748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:36.537207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.206510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.095731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:13.941441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:16.020207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:18.422855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:20.553263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:22.688573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:24.690973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.766369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:29.457054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:32.361560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:34.439000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:36.717154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.377787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.226809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:14.090372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:16.159580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:18.597207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:20.705182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:22.838437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:24.875648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:26.898592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:29.705976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:32.507103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:34.602848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:36.858380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.531486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.365949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:14.259663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:16.309453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:18.742052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:20.889219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.006266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:25.020620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:27.048203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:29.943193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:32.670710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:34.805392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:37.039882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.671544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.508408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:14.462053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:16.475026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:18.940595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:21.055833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.170702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:25.173915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:27.197577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:30.175489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:32.834433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:34.977547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:37.196485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.803795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.655758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:14.644079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:16.625831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:19.103319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:21.207491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.323126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:25.322886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:27.350060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:30.432757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.005858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:35.157592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:37.373670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:10.949136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:12.791814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:14.823953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:17.088644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:19.274494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:21.384180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:23.477077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:25.535306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:27.496176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:31.136508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:33.157419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-11-27T22:01:35.307019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-11-27T22:01:50.728604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Account Length (in months)AgeAvg Monthly GB DownloadChurn CategoryChurn LabelChurn ReasonContract TypeCustomer Service CallsDevice Protection & Online BackupExtra Data ChargesExtra International ChargesGenderGroupIntl ActiveIntl CallsIntl MinsIntl PlanLocal CallsLocal MinsMonthly ChargeNumber of Customers in GroupPayment MethodSeniorTotal ChargesUnder 30Unlimited Data Plan
Account Length (in months)1.0000.0120.0570.0230.3630.0280.504-0.2140.3640.0190.0170.0000.1350.0560.1070.1110.0000.8950.9080.1910.1350.0990.0050.8730.0000.022
Age0.0121.000-0.2130.0000.1280.0000.0260.0680.0480.0320.0180.0000.1440.0310.0160.0160.0000.0110.0100.144-0.1120.1000.9720.0750.9010.117
Avg Monthly GB Download0.057-0.2131.0000.0540.1030.0380.0710.0470.2270.1340.0490.0280.1280.0200.0300.0290.0300.0520.0530.4790.0050.0900.1630.2940.5080.415
Churn Category0.0230.0000.0541.0001.0000.9880.0320.0000.0670.0000.0370.0000.0600.1870.0490.0770.0000.0000.0110.1000.0220.0450.0000.0000.0000.112
Churn Label0.3630.1280.1031.0001.0001.0000.4520.6520.0610.0000.0790.0000.2480.1320.1050.1080.0070.2910.3300.2300.2480.2240.1210.1790.0400.169
Churn Reason0.0280.0000.0380.9881.0001.0000.0620.0340.0570.0000.0380.0000.0560.2030.0400.0570.0000.0000.0260.0890.0380.0750.0930.0000.0000.183
Contract Type0.5040.0260.0710.0320.4520.0621.0000.2070.2280.0160.1410.0000.1710.0540.1920.2110.0120.3840.4180.1220.1220.1190.0410.3070.0000.138
Customer Service Calls-0.2140.0680.0470.0000.6520.0340.2071.0000.0440.0330.1570.0140.1690.1090.0600.0600.000-0.200-0.2020.129-0.1570.0990.089-0.1250.0180.096
Device Protection & Online Backup0.3640.0480.2270.0670.0610.0570.2280.0441.0000.0890.1070.0160.0240.0000.1300.1410.0210.2980.3190.4390.0190.0790.0360.5080.0000.294
Extra Data Charges0.0190.0320.1340.0000.0000.0000.0160.0330.0891.0000.0250.0000.0210.0000.0200.0170.0000.0130.0100.130-0.0350.0260.0330.0810.0310.420
Extra International Charges0.0170.0180.0490.0370.0790.0380.1410.1570.1070.0251.0000.0000.0340.5640.8010.8030.1490.0140.0130.075-0.0230.0310.0030.0420.0000.019
Gender0.0000.0000.0280.0000.0000.0000.0000.0140.0160.0000.0001.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.000
Group0.1350.1440.1280.0600.2480.0560.1710.1690.0240.0210.0340.0001.0000.0070.0530.0640.0000.1080.1230.2751.0000.1040.1300.0730.0300.130
Intl Active0.0560.0310.0200.1870.1320.2030.0540.1090.0000.0000.5640.0000.0071.0000.5920.7340.2310.0330.0340.0380.0130.0230.0260.0260.0000.000
Intl Calls0.1070.0160.0300.0490.1050.0400.1920.0600.1300.0200.8010.0000.0530.5921.0000.9930.1990.0950.0960.0460.0130.0370.0270.1010.0000.033
Intl Mins0.1110.0160.0290.0770.1080.0570.2110.0600.1410.0170.8030.0000.0640.7340.9931.0000.2250.0980.0990.0470.0130.0290.0320.1040.0000.000
Intl Plan0.0000.0000.0300.0000.0070.0000.0120.0000.0210.0000.1490.0000.0000.2310.1990.2251.0000.0220.0000.0000.0130.0280.0000.0000.0240.000
Local Calls0.8950.0110.0520.0000.2910.0000.384-0.2000.2980.0130.0140.0280.1080.0330.0950.0980.0221.0000.9850.1740.1230.0730.0410.8030.0000.025
Local Mins0.9080.0100.0530.0110.3300.0260.418-0.2020.3190.0100.0130.0000.1230.0340.0960.0990.0000.9851.0000.1780.1250.0830.0370.8130.0000.027
Monthly Charge0.1910.1440.4790.1000.2300.0890.1220.1290.4390.1300.0750.0000.2750.0380.0460.0470.0000.1740.1781.000-0.2830.2130.1870.5960.0530.686
Number of Customers in Group0.135-0.1120.0050.0220.2480.0380.122-0.1570.019-0.035-0.0230.0001.0000.0130.0130.0130.0130.1230.125-0.2831.0000.0730.129-0.0220.0320.130
Payment Method0.0990.1000.0900.0450.2240.0750.1190.0990.0790.0260.0310.0000.1040.0230.0370.0290.0280.0730.0830.2130.0731.0000.1270.1160.0410.200
Senior0.0050.9720.1630.0000.1210.0930.0410.0890.0360.0330.0030.0000.1300.0260.0270.0320.0000.0410.0370.1870.1290.1271.0000.0890.2310.107
Total Charges0.8730.0750.2940.0000.1790.0000.307-0.1250.5080.0810.0420.0000.0730.0260.1010.1040.0000.8030.8130.596-0.0220.1160.0891.0000.0000.315
Under 300.0000.9010.5080.0000.0400.0000.0000.0180.0000.0310.0000.0000.0300.0000.0000.0000.0240.0000.0000.0530.0320.0410.2310.0001.0000.029
Unlimited Data Plan0.0220.1170.4150.1120.1690.1830.1380.0960.2940.4200.0190.0000.1300.0000.0330.0000.0000.0250.0270.6860.1300.2000.1070.3150.0291.000

Missing values

2024-11-27T22:01:37.664926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T22:01:38.236042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-27T22:01:38.749408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer IDChurn LabelAccount Length (in months)Local CallsLocal MinsIntl CallsIntl MinsIntl ActiveIntl PlanExtra International ChargesCustomer Service CallsAvg Monthly GB DownloadUnlimited Data PlanExtra Data ChargesStatePhone NumberGenderAgeUnder 30SeniorGroupNumber of Customers in GroupDevice Protection & Online BackupContract TypePayment MethodMonthly ChargeTotal ChargesChurn CategoryChurn Reason
04444-BZPU0138.00.00.0000.00310KS382-465703500000011010NaNNaN
15676-PTZX033179431.30.00.0000.00310OH371-7191149000011221703NaNNaN
28532-ZEKQ04482217.60.00.0010.00310OH375-99991510000111231014NaNNaN
31314-SMPJ01047111.660.071.0110.00210MO329-9001041000000217177NaNNaN
42956-TXCJ062184621.2310.0694.4110.00310WV330-81731510000011281720NaNNaN
59152-DEPY01768120.70.00.0000.00000RI344-940312310000209156NaNNaN
61958-SDSO057428849.20.00.0000.00510IA363-11071380000110472671NaNNaN
78787-QZUC02554203.70.00.0000.001210IA366-92381291000101471197NaNNaN
87768-OQJE070171627.40.00.0000.00110NY351-72690470000120523593NaNNaN
97716-RHEB050206445.80.00.0000.00000ID350-8884061000001211539NaNNaN
Customer IDChurn LabelAccount Length (in months)Local CallsLocal MinsIntl CallsIntl MinsIntl ActiveIntl PlanExtra International ChargesCustomer Service CallsAvg Monthly GB DownloadUnlimited Data PlanExtra Data ChargesStatePhone NumberGenderAgeUnder 30SeniorGroupNumber of Customers in GroupDevice Protection & Online BackupContract TypePayment MethodMonthly ChargeTotal ChargesChurn CategoryChurn Reason
66772424-WMEU13657144.20.00.0000.00610KY336-3020156001210116570CompetitorCompetitor offered more data
66787223-IAZO11167161.80.00.0010.02800TX276-9423032001600122241PriceExtra data charges
66792044-UCGH12289285.70.00.0000.04610AK316-9298185011200217377OtherMoved
66803059-FTFJ11713.00.00.0000.01610MO286-771413300120021313PriceExtra data charges
66818708-NXSF11412.00.00.0000.01610WV337-510417601150001414DissatisfactionService dissatisfaction
66822940-QHVU13616.80.00.0000.00410SC362-989504200120021952CompetitorCompetitor offered higher download speeds
66833033-TMYG11715.00.00.0000.051710KY378-992612410131012020CompetitorCompetitor offered higher download speeds
66847029-XDVM162046.90.00.0000.041010NE328-3647148001610218108CompetitorCompetitor made better offer
66856614-NAJG13615.40.00.0000.02510MN346-827504500150001546AttitudeAttitude of support person
66865104-AGDX11715.00.00.0000.011004IN257-5893122101600199CompetitorCompetitor made better offer